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Record W4409337363 · doi:10.5334/ijic.icic24046

Goal Setting within Care Environments for Individuals with Intellectual and Developmental Disabilities

2025· article· en· W4409337363 on OpenAlexaboutno aff
Megann Dong, Brian Dunne, Donnie Antony, Ruth Armstrong, Bridget Ryan, Maria Mathews, Shannon L. Sibbald

Bibliographic record

VenueInternational Journal of Integrated Care · 2025
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsnot available
Fundersnot available
KeywordsIntellectual disabilityPsychologyNursingGerontologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Background: Effective person-oriented goal setting in care environments is challenging owing to the complexity of the environment, and the requirement of supportive person-centred care. Health and social care settings, such as the community-care sector, aim to holistically address persons’ care needs, however, much of the guidance in the literature focuses explicitly on health. Effective person-centred care should consider all goals of the person, whether these goals focus on career, relationship, and/or health domains. Person-centred care responds to a person’s wants/needs, with a consideration for how these revolve around their goals. However, little is known about how community-care organizations use tools such as person-centred planning (PCP) to meet the needs and goals of persons-supported. Aim and Methods: To understand how a person-centred participatory goal setting process is carried out in a care environment, we used an integrated knowledge translation approach to collaborate with community care leaders. Our co-designed qualitative descriptive research project explored a community-care’s approach to person-centred goal setting in Ontario, Canada. We conducted 11 semi-structured interviews with community-care staff to understand their perspective of the PCP process, including key components, facilitators, barriers, and impacts. Results: The interviews with staff provide a thorough understanding of the PCP process used by the community-care organization from beginning to end, including the creation and implementation of PCPs. Five themes were strongly exemplified in our study: organizational culture, flexibility, accountability, utilizing staff characteristics to their maximum potential, and the positive impacts of PCPs. The PCPs displayed benefits not only for the persons-supported, but for the people who support and surround them, including family, friends, staff, and the wider community. Moreover, our study demonstrates how a community-care organization has been able to balance the needs of the organization and the persons they serve within a government-mandated planning process. Implications: Our study highlights how a community-care organization can facilitate person-centred services through PCPs and has implications for a wider uptake of PCPs amongst individuals with intellectual and developmental disabilities. The outcomes of this project will inform the spread of PCPs in community-care organizations and encourage other professionals to explore the use of PCPs in their own settings. Additionally, PCPs have the potential to be effectively implemented for other vulnerable populations. Next Steps: Future research should explore the use of a flexible framework to guide the use of PCPs in organizations. Moreover, governments should support organizations in evaluating their programs to support the spread of effective programs and to ensure that future implementation can be successful.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0100.011
Scholarly communication0.0050.002
Open science0.0020.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.331
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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